SOME ASYMPTOTIC PROPERTIES OF VARYING KERNEL DENSITY ESTIMATOR Robert Mnatsakanov Department of Statistics West Virginia University Abstract
In this talk we introduce a new nonparametric
estimator for probability density function defined on the non-negative
real line. Our construction is based on the inverse gamma density
function used as a kernel. It is shown that proposed estimator achieves
the optimal Integrated Mean Squared Error (IMSE) within the class of
non-negative estimators and is free from edge effect. The
L1-consistency of the proposed estimator is established as well.
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